Metadata control in a load-balanced distributed storage system

ABSTRACT

A plurality of computing devices are communicatively coupled to each other via a network, and each of the plurality of computing devices is operably coupled to one or more of a plurality of storage devices. A plurality of failure resilient address spaces are distributed across the plurality of storage devices such that each of the plurality of failure resilient address spaces spans a plurality of the storage devices. The plurality of computing devices maintains metadata that maps each failure resilient address space to one of the plurality of computing devices. The metadata is grouped into buckets. Each bucket is stored in a group of computing devices. However, only the leader of the group is able to directly access a particular bucket at any given time.

The present application is a continuation of U.S. application Ser. No.15/670,189, filed Aug. 7, 2017. This document is hereby incorporatedherein by reference in its entirety.

BACKGROUND

Limitations and disadvantages of conventional approaches to data storagewill become apparent to one of skill in the art, through comparison ofsuch approaches with some aspects of the present method and system setforth in the remainder of this disclosure with reference to thedrawings.

INCORPORATION BY REFERENCE

U.S. patent application Ser. No. 15/243,519 titled “Distributed ErasureCoded Virtual File System” is hereby incorporated herein by reference inits entirety.

BRIEF SUMMARY

Methods and systems are provided for metadata control in load-balanceddistributed storage system substantially as illustrated by and/ordescribed in connection with at least one of the figures, as set forthmore completely in the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates various example configurations of a virtual filesystem in accordance with aspects of this disclosure.

FIG. 2 illustrates an example configuration of a virtual file systemnode in accordance with aspects of this disclosure.

FIG. 3 illustrates another representation of a virtual file system inaccordance with an example implementation of this disclosure.

FIG. 4 illustrates an example of metadata bucket control in aload-balanced distributed storage system after a VFS is added inaccordance with an example implementation of this disclosure.

FIG. 5 illustrates an example of splitting metadata buckets in aload-balanced distributed storage system in accordance with an exampleimplementation of this disclosure.

FIG. 6 illustrates an example of metadata control in a load-balanceddistributed storage system after a VFS failure in accordance with anexample implementation of this disclosure.

FIG. 7 is flowcharts illustrating an example method for metadata controlin a load-balanced distributed storage system.

DETAILED DESCRIPTION

Traditionally, filesystems use a centralized control over the metadatastructure (e.g., directories, files, attributes, file contents). If alocal filesystem is accessible from a single server and that serverfails, the filesystem's data may be lost if as there is no furtherprotection. To add protection, some filesystems (e.g., as provided byNetApp) have used one or more pairs of controllers in an active-passivemanner to replicate the metadata across two or more computers. Othersolutions have used multiple metadata servers in a clustered way (e.g.,as provided by IBM GPFS, Dell EMC Isilon, Lustre, etc.). However,because the number of metadata servers in a traditional clustered systemis limited to small numbers, such systems are unable to scale.

The systems in this disclosure are applicable to small clusters and canalso scale to many, many thousands of nodes. An example embodiment isdiscussed regarding non-volatile memory (NVM), for example, flash memorythat comes in the form of a solid-state drive (SSD). The NVM may bedivided into 4 kB “blocks” and 128 MB “chunks.” “Extents” may be storedin volatile memory, e.g., RAM for fast access, backed up by NVM storageas well. An extent may store pointers for blocks, e.g., 256 pointers to1 MB of data stored in blocks. In other embodiments, larger or smallermemory divisions may also be used. Metadata functionality in thisdisclosure may be effectively spread across many servers. For example,in cases of “hot spots” where a large load is targeted at a specificportion of the filesystem's namespace, this load can be distributedacross a plurality of nodes.

FIG. 1 illustrates various example configurations of a virtual filesystem (VFS) in accordance with aspects of this disclosure. Shown inFIG. 1 is a local area network (LAN) 102 comprising one or more VFSnodes 120 (indexed by integers from 1 to J, for j≥1), and optionallycomprising (indicated by dashed lines): one or more dedicated storagenodes 106 (indexed by integers from 1 to M, for M≥1), one or morecompute nodes 104 (indexed by integers from 1 to N, for N≥1), and/or anedge router that connects the LAN 102 to a remote network 118. Theremote network 118 optionally comprises one or more storage services 114(indexed by integers from 1 to K, for K≥1), and/or one or more dedicatedstorage nodes 115 (indexed by integers from 1 to L, for L≥1).

Each VFS node 120 _(j) (j an integer, where 1≤j≤J) is a networkedcomputing device (e.g., a server, personal computer, or the like) thatcomprises circuitry for running VFS processes and, optionally, clientprocesses (either directly on an operating system of the device 104 _(n)and/or in one or more virtual machines running in the device 104 _(n)).

The compute nodes 104 are networked devices that may run a VFS frontendwithout a VFS backend. A compute node 104 may run VFS frontend by takingan SR-IOV into the NIC and consuming a complete processor core.Alternatively, the compute node 104 may run the VFS frontend by routingthe networking through a Linux kernel networking stack and using kernelprocess scheduling, thus not having the requirement of a full core. Thisis useful if a user does not want to allocate a complete core for theVFS or if the networking hardware is incompatible with the VFSrequirements.

FIG. 2 illustrates an example configuration of a VFS node in accordancewith aspects of this disclosure. A VFS node comprises a VFS frontend 202and driver 208, a VFS memory controller 204, a VFS backend 206, and aVFS SSD agent 214. As used in this disclosure, a “VFS process” is aprocess that implements one or more of: the VFS frontend 202, the VFSmemory controller 204, the VFS backend 206, and the VFS SSD agent 214.Thus, in an example implementation, resources (e.g., processing andmemory resources) of the VFS node may be shared among client processesand VFS processes. The processes of the VFS may be configured to demandrelatively small amounts of the resources to minimize the impact on theperformance of the client applications. The VFS frontend 202, the VFSmemory controller 204, and/or the VFS backend 206 and/or the VFS SSDagent 214 may run on a processor of the host 201 or on a processor ofthe network adaptor 218. For a multi-core processor, different VFSprocess may run on different cores, and may run a different subset ofthe services. From the perspective of the client process(es) 212, theinterface with the virtual file system is independent of the particularphysical machine(s) on which the VFS process(es) are running. Clientprocesses only require driver 208 and frontend 202 to be present inorder to serve them.

The VFS node may be implemented as a single tenant server (e.g.,bare-metal) running directly on an operating system or as a virtualmachine (VM) and/or container (e.g., a Linux container (LXC)) within abare-metal server. The VFS may run within an LXC container as a VMenvironment. Thus, inside the VM, the only thing that may run is the LXCcontainer comprising the VFS. In a classic bare-metal environment, thereare user-space applications and the VFS runs in an LXC container. If theserver is running other containerized applications, the VFS may runinside an LXC container that is outside the management scope of thecontainer deployment environment (e.g. Docker).

The VFS node may be serviced by an operating system and/or a virtualmachine monitor (VMM) (e.g., a hypervisor). The VMM may be used tocreate and run the VFS node on a host 201. Multiple cores may resideinside the single LXC container running the VFS, and the VFS may run ona single host 201 using a single Linux kernel. Therefore, a single host201 may comprise multiple VFS frontends 202, multiple VFS memorycontrollers 204, multiple VFS backends 206, and/or one or more VFSdrivers 208. A VFS driver 208 may run in kernel space outside the scopeof the LXC container.

A single root input/output virtualization (SR-IOV) PCIe virtual functionmay be used to run the networking stack 210 in user space 222. SR-IOVallows the isolation of PCI Express, such that a single physical PCIExpress can be shared on a virtual environment and different virtualfunctions may be offered to different virtual components on a singlephysical server machine. The I/O stack 210 enables the VFS node tobypasses the standard TCP/IP stack 220 and communicate directly with thenetwork adapter 218. A Portable Operating System Interface for uniX(POSIX) VFS functionality may be provided through lockless queues to theVFS driver 208. SR-IOV or full PCIe physical function address may alsobe used to run non-volatile memory express (NVMe) driver 214 in userspace 222, thus bypassing the Linux IO stack completely. NVMe may beused to access non-volatile storage media 216 attached via a PCI Express(PCIe) bus. The non-volatile storage media 220 may be, for example,flash memory that comes in the form of a solid-state drive (SSD) orStorage Class Memory (SCM) that may come in the form of an SSD or amemory module (DIMM). Other example may include storage class memorytechnologies such as 3D-XPoint.

The SSD may be implemented as a networked device by coupling thephysical SSD 216 with the SSD agent 214 and networking 210.Alternatively, the SSD may be implemented as a network-attached NVMe SSD222 or 224 by using a network protocol such as NVMe-oF (NVMe overFabrics). NVMe-oF may allow access to the NVMe device using redundantnetwork links, thereby providing a higher level or resiliency. Networkadapters 226, 228, 230 and 232 may comprise hardware acceleration forconnection to the NVMe SSD 222 and 224 to transform them into networkedNVMe-oF devices without the use of a server. The NVMe SSDs 222 and 224may each comprise two physical ports, and all the data may be accessedthrough either of these ports.

Each client process/application 212 may run directly on an operatingsystem or may run in a virtual machine and/or container serviced by theoperating system and/or hypervisor. A client process 212 may read datafrom storage and/or write data to storage in the course of performingits primary function. The primary function of a client process 212,however, is not storage-related (i.e., the process is only concernedthat its data is reliably stored and is retrievable when needed, and notconcerned with where, when, or how the data is stored). Exampleapplications which give rise to such processes include: email servers,web servers, office productivity applications, customer relationshipmanagement (CRM), animated video rendering, genomics calculation, chipdesign, software builds, and enterprise resource planning (ERP).

A client application 212 may make a system call to the kernel 224 whichcommunicates with the VFS driver 208. The VFS driver 208 puts acorresponding request on a queue of the VFS frontend 202. If several VFSfrontends exist, the driver may load balance accesses to the differentfrontends, making sure a single file/directory is always accessed viathe same frontend. This may be done by “sharding” the frontend based onthe ID of the file or directory. The VFS frontend 202 provides aninterface for routing file system requests to an appropriate VFS backendbased on the bucket that is responsible for that operation. Theappropriate VFS backend may be on the same host or it may be on anotherhost.

The VFS backend 206 hosts several buckets, each one of them services thefile system requests that it receives and carries out tasks to otherwisemanage the virtual file system (e.g., load balancing, journaling,maintaining metadata, caching, moving of data between tiers, removingstale data, correcting corrupted data, etc.)

The VFS SSD agent 214 handles interactions with a respective storagedevice 216. This may include, for example, translating addresses, andgenerating the commands that are issued to the storage device (e.g., ona SATA, SAS, PCIe, or other suitable bus). Thus, the VFS SSD agent 214operates as an intermediary between a storage device 216 and the VFSbackend 206 of the virtual file system. The SSD agent 214 could alsocommunicate with a standard network storage device supporting a standardprotocol such as NVMe-oF (NVMe over Fabrics).

FIG. 3 illustrates another representation of a virtual file system inaccordance with an example implementation of this disclosure. In FIG. 3, the element 302 represents memory resources (e.g., DRAM and/or othershort-term memory) and processing (e.g., x86 processor(s), ARMprocessor(s), NICs, ASICs, FPGAs, and/or the like) resources of variousnode(s) (compute, storage, and/or VFS) on which resides a virtual filesystem, such as described regarding FIG. 2 above. The element 308represents the one or more physical storage devices 216 which providethe long term storage of the virtual file system.

As shown in FIG. 3 , the physical storage is organized into a pluralityof distributed failure resilient address spaces (DFRASs) 518. Each ofwhich comprises a plurality of chunks 310, which in turn comprises aplurality of blocks 312. The organization of blocks 312 into chunks 310is only a convenience in some implementations and may not be done in allimplementations. Each block 312 stores committed data 316 (which maytake on various states, discussed below) and/or metadata 314 thatdescribes or references committed data 316.

The organization of the storage 308 into a plurality of DFRASs enableshigh performance parallel commits from many—perhaps all—of the nodes ofthe virtual file system (e.g., all nodes 104 ₁-104 _(N), 106 ₁-106 _(M),and 120 ₁-120 _(J) of FIG. 1 may perform concurrent commits inparallel). In an example implementation, each of the nodes of thevirtual file system may own a respective one or more of the plurality ofDFRAS and have exclusive read/commit access to the DFRASs that it owns.

Each bucket owns a DFRAS, and thus does not need to coordinate with anyother node when writing to it. Each bucket may build stripes across manydifferent chunks on many different SSDs, thus each bucket with its DFRAScan choose what “chunk stripe” to write to currently based on manyparameters, and there is no coordination required in order to do so oncethe chunks are allocated to that bucket. All buckets can effectivelywrite to all SSDs without any need to coordinate.

Each DFRAS being owned and accessible by only its owner bucket that runson a specific node allows each of the nodes of the VFS to control aportion of the storage 308 without having to coordinate with any othernodes (except during [re]assignment of the buckets holding the DFRASsduring initialization or after a node failure, for example, which may beperformed asynchronously to actual reads/commits to storage 308). Thus,in such an implementation, each node may read/commit to its buckets'DFRASs independently of what the other nodes are doing, with norequirement to reach any consensus when reading and committing tostorage 308. Furthermore, in the event of a failure of a particularnode, the fact the particular node owns a plurality of buckets permitsmore intelligent and efficient redistribution of its workload to othernodes (rather the whole workload having to be assigned to a single node,which may create a “hot spot”). In this regard, in some implementationsthe number of buckets may be large relative to the number of nodes inthe system such that any one bucket may be a relatively small load toplace on another node. This permits fine grained redistribution of theload of a failed node according to the capabilities and capacity of theother nodes (e.g., nodes with more capabilities and capacity may begiven a higher percentage of the failed nodes buckets).

To permit such operation, metadata may be maintained that maps eachbucket to its current owning node such that reads and commits to storage308 can be redirected to the appropriate node.

Load distribution is possible because the entire filesystem metadataspace (e.g., directory, file attributes, content range in the file,etc.) can be broken (e.g., chopped or sharded) into small, uniformpieces (e.g., “shards”). For example, a large system with 30 k serverscould chop the metadata space into 128 k or 256 k shards.

Each such metadata shard may be maintained in a “bucket.” Each VFS nodemay have responsibility over several buckets. When a bucket is servingmetadata shards on a given backend, the bucket is considered “active” orthe “leader” of that bucket. Typically, there are many more buckets thanVFS nodes. For example, a small system with 6 nodes could have 120buckets, and a larger system with 1,000 nodes could have 8 k buckets.

Each bucket may be active on a small set of nodes, typically 5 nodesthat that form a penta-group for that bucket. The cluster configurationkeeps all participating nodes up-to-date regarding the penta-groupassignment for each bucket.

Each penta-group monitors itself. For example, if the cluster has 10 kservers, and each server has 6 buckets, each server will only need totalk with 30 different servers to maintain the status of its buckets (6buckets will have 6 penta-groups, so 6*5=30). This is a much smallernumber than if a centralized entity had to monitor all nodes and keep acluster-wide state. The use of penta-groups allows performance to scalewith bigger clusters, as nodes do not perform more work when the clustersize increases. This could pose a disadvantage that in a “dumb” mode asmall cluster could actually generate more communication than there arephysical nodes, but this disadvantage is overcome by sending just asingle heartbeat between two servers with all the buckets they share (asthe cluster grows this will change to just one bucket, but if you have asmall 5 server cluster then it will just include all the buckets in allmessages and each server will just talk with the other 4). Thepenta-groups may decide (i.e., reach consensus) using an algorithm thatresembles the Raft consensus algorithm.

Each bucket may have a group of compute nodes that can run it. Forexample, five VFS nodes can run one bucket. However, only one of thenodes in the group is the controller/leader at any given moment.Further, no two buckets share the same group, for large enough clusters.If there are only 5 or 6 nodes in the cluster, most buckets may sharebackends. In a reasonably large cluster there many distinct node groups.For example, with 26 nodes, there are more than

$64\text{,}000\left( \frac{26!}{{5!}*{\left( {26 - 5} \right)!}} \right)$possible five-node groups (i.e., penta-groups).

All nodes in a group know and agree (i.e., reach consensus) on whichnode is the actual active controller (i.e., leader) of that bucket. Anode accessing the bucket may remember (“cache”) the last node that wasthe leader for that bucket out of the (e.g., five) members of a group.If it accesses the bucket leader, the bucket leader performs therequested operation. If it accesses a node that is not the currentleader, that node indicates the leader to “redirect” the access. Ifthere is a timeout accessing the cached leader node, the contacting nodemay try a different node of the same penta-group. All the nodes in thecluster share common “configuration” of the cluster, which allows thenodes to know which server may run each bucket.

Each bucket may have a load/usage value that indicates how heavily thebucket is being used by applications running on the filesystem. Forexample, a server node with 11 lightly used buckets may receive anotherbucket of metadata to run before a server with 9 heavily used buckets,even though there will be an imbalance in the number of buckets used.Load value may be determined according to average response latencies,number of concurrently run operations, memory consumed or other metrics.

Redistribution may also occur even when a VFS node does not fail. If thesystem identifies that one node is busier than the others based on thetracked load metrics, the system can move (i.e., “fail over”) one of itsbuckets to another server that is less busy. However, before actuallyrelocating a bucket to a different host, load balancing may be achievedby diverting writes and reads. Since each write may end up on adifferent group of nodes, decided by the DFRAS, a node with a higherload may not be selected to be in a stripe to which data is beingwritten. The system may also opt to not serve reads from a highly loadednode. For example, a “degraded mode read” may be performed, wherein ablock in the highly loaded node is reconstructed from the other blocksof the same stripe. A degraded mode read is a read that is performed viathe rest of the nodes in the same stripe, and the data is reconstructedvia the failure protection. A degraded mode read may be performed whenthe read latency is too high, as the initiator of the read may assumethat that node is down. If the load is high enough to create higher readlatencies, the cluster may revert to reading that data from the othernodes and reconstructing the needed data using the degraded mode read.

Each bucket manages its own distributed erasure coding instance (i.e.,DFRAS 518) and does not need to cooperate with other buckets to performread or write operations. There are potentially thousands of concurrent,distributed erasure coding instances working concurrently, each for thedifferent bucket. This is an integral part of scaling performance, as iteffectively allows any large filesystem to be divided into independentpieces that do not need to be coordinated, thus providing highperformance regardless of the scale.

Each bucket handles all the file systems operations that fall into itsshard. For example, the directory structure, file attributes and filedata ranges will fall into a particular bucket's jurisdiction.

An operation done from any frontend starts by finding out what bucketowns that operation. Then the backend leader, and the node, for thatbucket is determined. This determination may be performed by trying thelast-known leader. If the last-known leader is not the current leader,that node may know which node is the current leader. If the last-knownleader is not part of the bucket's penta-group anymore, that backendwill let the front end know that it should go back to the configurationto find a member of the bucket's penta-group. The distribution ofoperations allows complex operations to be handled by a plurality ofservers, rather than by a single computer in a standard system.

If the cluster of size is small (e.g., 5) and penta-groups are used,there will be buckets that share the same group. As the cluster sizegrows, buckets are redistributed such that no two groups are identical.

FIG. 4 illustrates an example of metadata bucket control in aload-balanced distributed storage system after a VFS is added inaccordance with an example implementation of this disclosure. Withrespect to FIGS. 4, 5, and 6 , a three-member group is assumed forillustrative purposes. As discussed above, a group of VFS's may be anynumber. Because no two buckets in this example share the same group, thebuckets can also be labeled according to their group. With four nodes,there are

$4\left( \frac{4!}{{3!}*{\left( {4 - 3} \right)!}} \right)$possible three-node groups. With five nodes, there are

$10\left( \frac{5!}{{3!}*{\left( {5 - 3} \right)!}} \right)$possible three-node groups. With six nodes, there are

$20\left( \frac{6!}{{3!}*{\left( {6 - 3} \right)!}} \right)$possible three-node groups.

FIG. 4 illustrates five VFS backends 206 ₁-206 ₅. The first four VFSbackends 206 ₁-206 ₄ are present initially. The last backend VFS backend206 ₅ is added later. Four unique groups (A, B, C, and D) are initiallyformed in a cluster of the first four VFS backends 206 ₁-206 ₄.

VFS backend 206 ₁ initially comprises a group A bucket, a group B bucketand a group C bucket. The VFS comprising VFS backend 206 ₁ is the leaderof group C and is the only VFS that can access the DFRAS associated withthe metadata in bucket C. VFS backend 206 ₂ initially comprises a groupA bucket, a group B bucket and a group D bucket, and the VFS comprisingVFS backend 206 ₂ is the leader of group A and is the only VFS that canaccess the DFRAS associated with the metadata in bucket A. VFS backend206 ₃ initially comprises a group A bucket, a group C bucket and a groupD bucket, and the VFS comprising VFS backend 206 ₃ is the leader ofgroup D and is the only VFS that can access the DFRAS associated withthe metadata in bucket D. VFS backend 206 ₄ initially comprises a groupB bucket, a group C bucket and a group D bucket, and the VFS comprisingVFS backend 206 ₄ is the leader of group B and is the only VFS that canaccess the DFRAS associated with the metadata in bucket B. Also, if abackend is not a group leader, it can identify the group leader.

Each time a new backend node joins the cluster, the groups for eachbucket may be reconfigured, and the new groups' configuration iscommunicated to all current nodes. The configuration change may beperformed such that the leader node of each group does not change, andno leader changes may be allowed while that new node is being added.This ensures that the overhead of such operation does not limit theoverall performance of the system and does not impact the scalability.

After the new group's configuration is sent to all the nodes, it isdeemed active, and that new node can now control some buckets as well.That new node will be part of one or more groups and may takeresponsibility for some of the buckets in order to make the cluster mostload-balanced.

Several nodes may be added concurrently with one configurationre-calculation and communication. When all of the new nodes are added,workload may then re-spread across the new cluster's resources for allthe buckets that are required for rebalancing. It may take a fewload-balancing iterations for the system to stabilize to an optimalstate when multiple nodes are added.

When the VFS backend 206 ₅ joins the cluster, the groups for each bucketre-calculated, and the new group's configuration is communicated to allcurrent nodes. As illustrated in FIG. 4 , for example, bucket A from VFSbackend 206 ₃ and bucket C from VFS backend 206 ₄ may be moved into VFSbackend 206 ₅ as a result of this reconfiguration.

Each bucket may be split in half to double the number of buckets. Byincreasing the number of buckets, the system is able to scale to supporta bigger cluster of nodes. Also, if a single bucket ends up very busy(e.g., hot spotted), the buckets can be split in half until the singlebusiest bucket becomes small enough for one CPU core. Even though thedata is cryptographically hashed and uniformly sharded across thebuckets, still there could be a case where the workload is able to“attack” a single bucket with a lot of work. Being able to perform thissplitting allows a relief for such cases.

FIG. 5 illustrates an example of splitting metadata buckets in aload-balanced distributed storage system in accordance with an exampleimplementation of this disclosure. FIG. 5 also illustrates an examplewhere one backend is the leader for more than one bucket. For example,the second VFS backend 206 ₂ is the leader for buckets A1 and C2. Andbuckets B1 and D1 are in the “standby” mode in VFS backend 206 ₂.

The initial cluster of FIG. 4 , comprising four VFS backends 206 ₁-206 ₄and group A, B, C and D buckets is assumed to be the starting point ofFIG. 5 . FIG. 5 adds two new VFS backends 206 ₅ and 206 ₆. Then, thegroup A, B, C and D buckets are each split, thereby forming group A1,A2, B1, B2, C1, C2, D1 and D2 buckets. The previous leaders of group A,B, C and D buckets are the leaders of group A1, B1, C1 and D1 bucketsrespectively. Group A2, B2, C2 and D2 buckets will start off being ledby the same backends to preserve continuous operation, but are going tobe quickly redistributed according to load into the four original VFSbackends 206 ₁-206 ₄ and the two new VFS backends 206 ₅ and 206 ₆.

If a server fails, that server's metadata shards (for which the serveris the bucket leader) are moved to other servers based on their load.The redistribution can be determined according to how many buckets eachnode controls. For example, if each VFS node in a system controls 10active buckets of metadata and one of the servers fails, the workload of10 other servers will increase by 10%. The redistribution can also bedetermined according to actual load, similarly to a load balancingoperation. The redistribution of buckets from a failed server may firstfill the servers with the lowest current load. Load can be calculated byamount of operations each bucket handles, or by the latency of thatbucket as perceived by its peers. Observing load by effective latency topeers accounts for cases outside the control of the system, such ascongested networking links, etc. By moving the active buckets of thefailed servers to the least busy servers, the load variability acrossall servers may be reduced. The ability to reduce load variabilitycontributes to linear performance scalability.

FIG. 6 illustrates an example of metadata control in a load-balanceddistributed storage system after a VFS failure in accordance with anexample implementation of this disclosure. Assume that VFS backends 206₃ in FIG. 5 fails. As illustrated in FIG. 6 , the buckets A1, C1, D1 andB2 are redistributed to VFS backends 206 ₄, 206 ₆, 206 ₅, and 206 ₁respectively.

FIG. 7 is flowcharts illustrating an example method for metadata controlin a load-balanced distributed storage system. In block 702, metadatashards are distributed into buckets, where each bucket is associatedwith a unique group of VFS backends having a known leader. The serversin each group decide among themselves which is best to run the buckets.There a lot of parallel small decisions, no central authority, as we tryto have as little central decisions as we can. Once we let all nodesknow what is the option to run (the centralized configuration of whatnodes are present and the groups), all decisions become local to theirgroups. As discussed above, the group leader is the only VFS backendwith the ability to access the memory associated with a particularmetadata shard.

In block 704, the bucket leaders monitor the load balance across thebackends and determine whether changing group leadership and/orsplitting buckets can improve the load balance. In block 706, if a VFSbackend fails, the buckets on that backend are redistributed. In block708, if one or more VFS backends are added, the buckets from theprevious VFS backends are redistributed into the new VFS backendswithout changing group leadership.

Each backend compares its own load (and its perceived load by thelatency it provides to its peers) to the other backends it sharesbuckets with. If a backend notices that its load is too high, it wouldlook across its lead bucket and all its peers and optimize what bucketmove would make most sense. Even if the cluster comprises a large numberof servers, each such local improvement may involve a smaller number ofservers.

While the present method and/or system has been described with referenceto certain implementations, it will be understood by those skilled inthe art that various changes may be made and equivalents may besubstituted without departing from the scope of the present methodand/or system. In addition, many modifications may be made to adapt aparticular situation or material to the teachings of the presentdisclosure without departing from its scope. Therefore, it is intendedthat the present method and/or system not be limited to the particularimplementations disclosed, but that the present method and/or systemwill include all implementations falling within the scope of theappended claims.

As utilized herein the terms “circuits” and “circuitry” refer tophysical electronic components (i.e. hardware) and any software and/orfirmware (“code”) which may configure the hardware, be executed by thehardware, and or otherwise be associated with the hardware. As usedherein, for example, a particular processor and memory may comprisefirst “circuitry” when executing a first one or more lines of code andmay comprise second “circuitry” when executing a second one or morelines of code. As utilized herein, “and/or” means any one or more of theitems in the list joined by “and/or”. As an example, “x and/or y” meansany element of the three-element set {(x), (y), (x, y)}. In other words,“x and/or y” means “one or both of x and y”. As another example, “x, y,and/or z” means any element of the seven-element set {(x), (y), (z), (x,y), (x, z), (y, z), (x, y, z)}. In other words, “x, y and/or z” means“one or more of x, y and z”. As utilized herein, the term “exemplary”means serving as a non-limiting example, instance, or illustration. Asutilized herein, the terms “e.g.,” and “for example” set off lists ofone or more non-limiting examples, instances, or illustrations. Asutilized herein, circuitry is “operable” to perform a function wheneverthe circuitry comprises the necessary hardware and code (if any isnecessary) to perform the function, regardless of whether performance ofthe function is disabled or not enabled (e.g., by a user-configurablesetting, factory trim, etc.).

What is claimed is:
 1. A method for controlling memory access,comprising: distributing metadata into a plurality of buckets, wherein:each bucket is associated with a unique group of computing devices of aplurality of computing devices; the computing devices of each uniquegroup dynamically select upon a particular computing device as a leaderof the bucket associated with the unique group; and allowing access to aparticular address space only by a leader of a bucket to which metadataassociated with that particular address space has been distributed. 2.The method of claim 1, wherein the plurality of computing devicescomprises a plurality of virtual file system (VFS) nodes.
 3. The methodof claim 1, wherein each unique group of computing devices comprisesfive VFS nodes of a plurality of VFS nodes.
 4. The method of claim 2,wherein all VFS nodes in a unique group know and agree on a leader. 5.The method of claim 1, wherein each address space has only one leader atany given time.
 6. The method of claim 1, wherein each of the computingdevices is a leader of multiple buckets.
 7. The method of claim 1,wherein the method comprises, in the event of a failure of one of theplurality of computing devices, redistributing metadata that was on thefailed computing device.
 8. The method of claim 1, wherein the methodcomprises, in the event of a change in a number of computing devices inthe plurality of computing devices, redistributing metadata according toa load value associated with each computing device in the plurality ofcomputing devices.
 9. The method of claim 1, wherein the methodcomprises changing group leadership in the event of a load imbalance.10. The method of claim 1, wherein the method comprises splitting andredistributing buckets of the plurality of buckets in the event of aload imbalance.
 11. A system, the system comprising: a plurality ofcomputing devices; and a plurality of buckets configured to storemetadata, wherein: each bucket is associated with a unique group ofcomputing devices of the plurality of computing devices, one computingdevice of each unique group of computing devices is selected as a leaderof the bucket associated with the unique group, and access to aparticular address space is allowed only by a leader of a unique groupof computing devices associated with a bucket that stores metadataassociated with the particular address space.
 12. The system of claim11, wherein the plurality of computing devices comprises a plurality ofvirtual file system (VFS) nodes.
 13. The system of claim 11, whereineach unique group of computing devices comprises five VFS nodes of aplurality of VFS nodes.
 14. The system of claim 12, wherein all VFSnodes in a unique group know and agree on a leader.
 15. The system ofclaim 11, wherein each address space is associated with only one leaderat any given time.
 16. The system of claim 11, wherein each of thecomputing devices is a leader of multiple buckets.
 17. The system ofclaim 11, wherein in the event of a failure of one of the plurality ofcomputing devices, metadata that was on the failed computing device isredistributed.
 18. The system of claim 11, wherein in the event of achange in a number of computing devices in the plurality of computingdevices, metadata is redistributed according to a load value associatedwith each computing device in the plurality of computing devices. 19.The system of claim 11, wherein group leadership is changed in the eventof a load imbalance.
 20. The system of claim 11, wherein one or morebuckets of the plurality of buckets are split and redistributed in theevent of a load imbalance.